Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.
Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.
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DOI：
10.1371/journal.pone.0249502
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Hassanzadeh R;Silva RF;Abrol A;Salman M;Bonkhoff A;Du Y;Fu Z;DeRamus T;Damaraju E;Baker B;Calhoun VD
个体可以根据他们的大脑测量和活动在人群中进行表征，考虑到大脑解剖结构，结构-功能关系或生活经验的受试者间差异。许多神经影像学研究已经证明了从静息功能磁共振成像（fMRI）估计的功能网络连接模式的潜力，以区分群体和预测个体受试者的信息。然而，大脑连接网络的空间异质性中存在的预测信号还有待广泛研究。在这项研究中，我们调查，第一次，使用成对的关系之间的休息状态独立的空间地图来表征个人。为此，我们开发了一个深度连体框架，包括三维卷积神经网络，用于基于通过全自动fMRI独立成分分析方法估计的个体水平空间图进行对比学习。所提出的框架评估是否成对的空间网络（例如，视觉网络和听觉网络）能够在广泛的全脑分析中识别受试者并评估不同网络对的预测能力的空间变异性。我们对来自英国生物银行研究的近12，000名未受影响的个体进行的分析表明，所提出的方法可以区分测试集上单个网络对的准确性高达88%（最佳模型，经过几次运行），并且在皮层下域水平的平均准确性为82%，特别是达到的最高平均域水平准确性。对我们的网络学习特征的进一步研究显示，年轻大脑的预测准确性具有更高的空间变异性，男性的辨别力显着更高。总之，空间网络之间的关系似乎是既有信息和个人的歧视，并应进一步研究作为假定的基于大脑的生物标志物。
Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs’ predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network’s learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.
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NeuroMark：基于自动化和自适应 ICA 的管道，用于识别脑部疾病的可重复功能磁共振成像标记。
DOI：
10.1016/j.nicl.2020.102375
发表时间：
2020
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
34.7
作者：
Alexander-Bloch, Aaron;Giedd, Jay N.;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM